Despite great advances in both the understanding of the neurobiology of addiction and the development and approval of smoking cessation therapies, a significant need remains for better smoking cessation aids. Our goal is aligned with NIDA's intent to bring the power of science to bear on drug abuse and addiction. We propose to use tools from a broad range of disciplines, and promise rapid and effective dissemination and use of the results of the proposed research to significantly improve treatment of nicotine abuse and addiction. The platform we propose to use will help identify, evaluate, and develop innovative medications to treat nicotine abuse and addiction. We propose to implement a research program through collaborations with academia, industry and government. Although there are two first-line (varenicline and bupropion) and two second-line (clonidine and notriptyline) approved medications for smoking cessation that significantly help to stop smoking, about 80% of smokers are unable to remain abstinent. As an explanation for such low success rate, it has been hypothesized that addiction develops in the presence of predisposing cognitive and affective states, which are unaffected by existing therapeutics but could be targeted by new smoking cessation aids for improved efficacy. One of the main reasons for the slow development of novel medications with improved efficacy is the lack of clearly translatable preclinical models of nicotine dependence that exhibit high degrees of predictive validity. Most preclinical tests are simply based on blocking nicotine-like effects but ignore other predisposing or underlying factors, either cognitive or emotional, that may trigger and maintain nicotine abuse. The availability of both approved medications and failed compounds gives us the opportunity to create a battery of nicotine dependence and CNS efficacy tests with enhanced predictive validity, potentially a key tool in enhancing future discovery and development efforts. During Phase I we will develop a test battery based on 1) consideration of multiple aspects underlying abuse (rewarding effects of acute and chronic nicotine, alleviation of withdrawal, relapse, anxiety, depression, cognitive dysfunction and impulsivity), 2) definition of a smoking cessation predictive score through a machine learning algorithm trained on a behavioral dataset generated with both effective and ineffective medications in our test battery and 3) minimization of animal and throughput costs. During Phase II the platform will grow to comprise a database of compounds and mechanisms of action of postulated smoking cessation potential, prioritized by their smoking cessation scores and predicted superiority in combating emotional and cognitive aspects of nicotine dependence. Finally, this platform (battery, database and computational tools) will be offered during Phase III as drug screening method to the members of a private public partnership, created to maintain, support, further develop and publicize the platform. The novelty of this project resides in the combination of economic principles and bioinformatics methods to take advantage of existing smoking cessation FDA-approved gold standards, the inclusion of cognitive and emotional state-relevant testing in the proposed preclinical battery, the creation of a knowledge database, and the management of the final platform by a private-public consortium to ensure maximal quality, value and access. We expect that the knowledge and tools generate by this project will stimulate further research and drug development both for smoking cessation and across other areas of drug abuse and discovery.